UNICEF
Data Scientist Consultant, Health and HIV Unit, Data & Analytics, DAPM NYHQ, rem
UNICEF, New York, New York, us, 10261
Data Scientist Consultant, Health and HIV Unit, Data & Analytics, DAPM NYHQ, remote. Req#584737
About UNICEF
If you are a committed, creative professional and are passionate about making a lasting difference for children, UNICEF, the world’s leading children’s rights organization, invites you to apply. UNICEF operates in 190 countries/territories and focuses on child survival, protection and development. UNICEF is funded by voluntary contributions from individuals, businesses, foundations and governments.
Consultancy Title Data Scientist Consultancy
Division/Duty Station Health and HIV Unit/Data & Analytics/DAMP; Remote
Duration November 1, 2025 – October 30, 2026
Scope and Background UNICEF’s Data and Analytics team for Maternal, Newborn, Child, and Adolescent Health (MNCAH) strengthens global accountability and supports countries to use data for better health outcomes. The team develops global results frameworks, maintains the MNCAH Global Database, and provides technical support to improve data quality, analysis, and use. The Actionable Health Analytics for Decision-making (AHEAD) initiative aims to improve routine health data use across health systems, generating timely, subnational insights to guide policy and programming.
The Data Scientist Consultant will develop analytics pipelines and products to support MNCAH goals, embedded within the MNCAH Data and Analytics team and collaborating with regional/country teams. The consultant will help translate data into action to support more agile, accountable, and equitable health systems.
Responsibilities
Strengthen global data infrastructure, analytics tools, and documentation of best practices.
Provide technical support to country offices and ministries of health to access, clean, and analyze routine data.
Finalize and scale country-specific data pipelines, dashboards, and analytics products.
Collaborate with country offices and ministries of health to access, clean, and analyze RHIS and administrative data with a focus on PHC and MNCAH planning.
Customize country-specific analytics pipelines and visualizations to meet national and subnational decision-making needs.
Translate analytics into public health use cases (e.g., district performance tracking, bottleneck identification, facility readiness monitoring).
Design and maintain automated, scalable data pipelines to ingest, transform, and standardize data from diverse sources (e.g., DHIS2, MICS, WorldPop, administrative datasets).
Develop and operationalize core analytics functions, including coverage estimation, indicator computation, geospatial integration, and data quality checks.
Build modular pipeline components to ensure reusability across countries and rapid-cycle analytics needs.
Develop tailored outputs (dashboards, slide decks, summaries) for country reviews, planning meetings, and supervision visits.
Optimize visualization workflows for reproducibility, speed, and modularity across administrative levels and indicators.
Design simple user interfaces (e.g., Streamlit or Shiny) for non-technical users to run core analytics and generate outputs.
Support maintenance and expansion of the MNCAH Global Database and related data infrastructure, including automation of regular data refreshes.
Implement metadata tracking, version control, audit trails, and internal monitoring tools for datasets, scripts, and outputs.
Ensure data integrity and harmonization across indicators, geographies, and time periods.
AI-Supported Analytics and Automation: develop tools that expose outputs to large language models for natural language querying and integrate AI tools into the AHEAD analytics package for narrative generation and slide creation.
Collaborate with the AI engineering team to embed verified data and local interpretation into country-specific products.
Ensure reproducibility, documentation, and capacity strengthening, with version-controlled workflows and clear metadata.
Terms of Reference / Deliverables
Pipeline Development and Automation: integrate non-facility data into the database and ensure availability for analysis.
Onboard new data sources via scalable modules (target: ≥5 sources).
Incorporate geospatial layers into the database for population-based analysis.
Produce faster and robust coverage metrics using optimized numerators/denominators.
Integrate emerging datasets (e.g., HIV, climate, HR, conflict) as needed.
Implement automated data quality detection and transparent review of imputations/outliers.
Develop an AI prompting backend in Python; optimize prompting performance (caching/parallelization).
Deliver reproducible analyses across admin levels and reusable visualizations (PowerPoint-ready).
Establish monitoring alerts for ETL, AI query, and output generation; maintain auditable documentation of versions.
Qualifications Education
Bachelor’s degree in Data Science, Statistics, Computer Science, Epidemiology, Public Health, Engineering, Health Informatics, or related quantitative field.
Certifications in data engineering, data visualization, or advanced statistics (R) are assets.
Experience
Minimum 5 years in data science, data engineering, or health data analytics, with experience applying advanced data techniques to MNCAH or similar contexts.
Designing and maintaining reproducible data pipelines and automated reporting tools using R or Python.
Handling large RHIS (e.g., DHIS2), administrative, or survey data (e.g., DHS, MICS).
Developing statistical models and conducting data quality assessments (imputation, outlier detection, validation).
Building interactive dashboards and data visualizations; working with Git-based version control.
Experience supporting decision-makers in data-driven decisions, especially in LMICs.
Strong written and oral English; proficiency in another UN language desirable.
Familiarity with global health indicators, especially maternal, newborn, child health, PHC, and health systems performance.
Desirable
Experience with cloud-based databases (e.g., Azure Postgres, AWS RDS) and ETL pipelines integrating geospatial and facility data.
Understanding of data governance, privacy, and ethical use of health data.
Experience working in multicultural, interdisciplinary teams and coordinating across HQ/region/country levels.
Experience contributing to capacity-building via trainings, documentation, or mentoring.
Application Instructions The application should include three attachments: a cover letter, a CV, and a financial proposal with your name on the file. Submit via the online portal under supporting documents.
Additional Information UNICEF reserves rights, policies include disability accommodation, zero tolerance on exploitation/harassment, and mandatory background checks. Consultants are not staff and are responsible for taxes and visa/health insurance as applicable. Remote work may exempt some requirements.
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If you are a committed, creative professional and are passionate about making a lasting difference for children, UNICEF, the world’s leading children’s rights organization, invites you to apply. UNICEF operates in 190 countries/territories and focuses on child survival, protection and development. UNICEF is funded by voluntary contributions from individuals, businesses, foundations and governments.
Consultancy Title Data Scientist Consultancy
Division/Duty Station Health and HIV Unit/Data & Analytics/DAMP; Remote
Duration November 1, 2025 – October 30, 2026
Scope and Background UNICEF’s Data and Analytics team for Maternal, Newborn, Child, and Adolescent Health (MNCAH) strengthens global accountability and supports countries to use data for better health outcomes. The team develops global results frameworks, maintains the MNCAH Global Database, and provides technical support to improve data quality, analysis, and use. The Actionable Health Analytics for Decision-making (AHEAD) initiative aims to improve routine health data use across health systems, generating timely, subnational insights to guide policy and programming.
The Data Scientist Consultant will develop analytics pipelines and products to support MNCAH goals, embedded within the MNCAH Data and Analytics team and collaborating with regional/country teams. The consultant will help translate data into action to support more agile, accountable, and equitable health systems.
Responsibilities
Strengthen global data infrastructure, analytics tools, and documentation of best practices.
Provide technical support to country offices and ministries of health to access, clean, and analyze routine data.
Finalize and scale country-specific data pipelines, dashboards, and analytics products.
Collaborate with country offices and ministries of health to access, clean, and analyze RHIS and administrative data with a focus on PHC and MNCAH planning.
Customize country-specific analytics pipelines and visualizations to meet national and subnational decision-making needs.
Translate analytics into public health use cases (e.g., district performance tracking, bottleneck identification, facility readiness monitoring).
Design and maintain automated, scalable data pipelines to ingest, transform, and standardize data from diverse sources (e.g., DHIS2, MICS, WorldPop, administrative datasets).
Develop and operationalize core analytics functions, including coverage estimation, indicator computation, geospatial integration, and data quality checks.
Build modular pipeline components to ensure reusability across countries and rapid-cycle analytics needs.
Develop tailored outputs (dashboards, slide decks, summaries) for country reviews, planning meetings, and supervision visits.
Optimize visualization workflows for reproducibility, speed, and modularity across administrative levels and indicators.
Design simple user interfaces (e.g., Streamlit or Shiny) for non-technical users to run core analytics and generate outputs.
Support maintenance and expansion of the MNCAH Global Database and related data infrastructure, including automation of regular data refreshes.
Implement metadata tracking, version control, audit trails, and internal monitoring tools for datasets, scripts, and outputs.
Ensure data integrity and harmonization across indicators, geographies, and time periods.
AI-Supported Analytics and Automation: develop tools that expose outputs to large language models for natural language querying and integrate AI tools into the AHEAD analytics package for narrative generation and slide creation.
Collaborate with the AI engineering team to embed verified data and local interpretation into country-specific products.
Ensure reproducibility, documentation, and capacity strengthening, with version-controlled workflows and clear metadata.
Terms of Reference / Deliverables
Pipeline Development and Automation: integrate non-facility data into the database and ensure availability for analysis.
Onboard new data sources via scalable modules (target: ≥5 sources).
Incorporate geospatial layers into the database for population-based analysis.
Produce faster and robust coverage metrics using optimized numerators/denominators.
Integrate emerging datasets (e.g., HIV, climate, HR, conflict) as needed.
Implement automated data quality detection and transparent review of imputations/outliers.
Develop an AI prompting backend in Python; optimize prompting performance (caching/parallelization).
Deliver reproducible analyses across admin levels and reusable visualizations (PowerPoint-ready).
Establish monitoring alerts for ETL, AI query, and output generation; maintain auditable documentation of versions.
Qualifications Education
Bachelor’s degree in Data Science, Statistics, Computer Science, Epidemiology, Public Health, Engineering, Health Informatics, or related quantitative field.
Certifications in data engineering, data visualization, or advanced statistics (R) are assets.
Experience
Minimum 5 years in data science, data engineering, or health data analytics, with experience applying advanced data techniques to MNCAH or similar contexts.
Designing and maintaining reproducible data pipelines and automated reporting tools using R or Python.
Handling large RHIS (e.g., DHIS2), administrative, or survey data (e.g., DHS, MICS).
Developing statistical models and conducting data quality assessments (imputation, outlier detection, validation).
Building interactive dashboards and data visualizations; working with Git-based version control.
Experience supporting decision-makers in data-driven decisions, especially in LMICs.
Strong written and oral English; proficiency in another UN language desirable.
Familiarity with global health indicators, especially maternal, newborn, child health, PHC, and health systems performance.
Desirable
Experience with cloud-based databases (e.g., Azure Postgres, AWS RDS) and ETL pipelines integrating geospatial and facility data.
Understanding of data governance, privacy, and ethical use of health data.
Experience working in multicultural, interdisciplinary teams and coordinating across HQ/region/country levels.
Experience contributing to capacity-building via trainings, documentation, or mentoring.
Application Instructions The application should include three attachments: a cover letter, a CV, and a financial proposal with your name on the file. Submit via the online portal under supporting documents.
Additional Information UNICEF reserves rights, policies include disability accommodation, zero tolerance on exploitation/harassment, and mandatory background checks. Consultants are not staff and are responsible for taxes and visa/health insurance as applicable. Remote work may exempt some requirements.
#J-18808-Ljbffr